def execute(self):
# set various attributes to self.scan
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# Average data if necessary
self.scan = sdutil.doaverage(self.scan, self.scanaverage, self.timeaverage, self.tweight, self.polaverage, self.pweight)
# Reload plotter if necessary
sd.plotter._assert_plotter(action="reload")
# Set subplot layout
if self.subplot > 10:
row = int(self.subplot/10)
col = (self.subplot % 10)
sd.plotter.set_layout(rows=row,cols=col,refresh=False)
else:
if self.subplot > -1:
casalog.post(("Invalid subplot value, %d, is ignored. It should be in between 11 and 99." % self.subplot),priority="WARN")
sd.plotter.set_layout(refresh=False)
# Set subplot margins
if self.margin != sd.plotter._margins:
sd.plotter.set_margin(margin=self.margin,refresh=False)
# Actual plotting
getattr(self,'plot_%s'%(self.plottype))()
def execute(self):
# set various attributes to self.scan
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# Average data if necessary
self.scan = sdutil.doaverage(self.scan, self.scanaverage,
self.timeaverage, self.tweight,
self.polaverage, self.pweight)
# Reload plotter if necessary
sd.plotter._assert_plotter(action="reload")
# Set subplot layout
if self.subplot > 10:
row = int(self.subplot / 10)
col = (self.subplot % 10)
sd.plotter.set_layout(rows=row, cols=col, refresh=False)
else:
if self.subplot > -1:
casalog.post((
"Invalid subplot value, %d, is ignored. It should be in between 11 and 99."
% self.subplot),
priority="WARN")
sd.plotter.set_layout(refresh=False)
# Set subplot margins
if self.margin != sd.plotter._margins:
sd.plotter.set_margin(margin=self.margin, refresh=False)
# Actual plotting
getattr(self, 'plot_%s' % (self.plottype))()
def execute(self):
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# Average data if necessary
self.scan = sdutil.doaverage(self.scan, self.scanaverage, self.timeaverage, self.tweight, self.polaverage, self.pweight)
# restore self.fluxunit
self.fluxunit = self.fluxunit_saved
del self.fluxunit_saved
self.calc_statistics()
def execute(self):
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# Average data if necessary
self.scan = sdutil.doaverage(self.scan, self.scanaverage,
self.timeaverage, self.tweight,
self.polaverage, self.pweight)
# restore self.fluxunit
self.fluxunit = self.fluxunit_saved
del self.fluxunit_saved
self.calc_statistics()
def execute(self):
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# average data
self.scan = sdutil.doaverage(self.scan, self.scanaverage,
self.timeaverage, self.tweight,
self.polaverage, self.pweight)
# restore fluxunit
self.fluxunit = self.fluxunit_saved
del self.fluxunit_saved
self.result = dict.fromkeys(['nfit', 'peak', 'cent', 'fwhm'], [])
self.fitparams = []
if self.assert_no_channel_selection_in_spw(mode='result'):
maskline_dict = self.scan.parse_spw_selection(self.spw)
valid_spw_list = []
for (k, v) in maskline_dict.items():
if len(v) > 0 and numpy.all(numpy.array(map(len, v)) > 0):
valid_spw_list.append(k)
sel_org = self.scan.get_selection()
for irow in xrange(self.scan.nrow()):
scantab = self.scan.get_row(irow, insitu=False)
spw = scantab.getif(0)
if not spw in valid_spw_list:
continue
maskline = maskline_dict[spw]
self.maskline = maskline
self.__set_linelist(scantab)
self.__fit(scantab)
ifs_org = sel_org.get_ifs()
ifs_new = list(set(ifs_org) & set(valid_spw_list))
sel_org.set_ifs(ifs_new)
self.scan.set_selection(sel_org)
else:
self.maskline = []
self.__set_linelist()
self.__fit()
def execute(self):
# set various attributes to self.scan
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
#Average within each scan
self.scan = sdutil.doaverage(self.scan, self.scanaverage, self.timeaverage, self.tweight, self.polaverage, self.pweight, self.averageall)
engine = sdsmooth_engine(self)
# Smoothing
if self.kernel not in ['', 'none']:
engine.initialize()
# Actual implementation is defined outside the class
# since those are used in task_sdreduce.
engine.execute()
engine.finalize()
def execute(self):
self.set_to_scan()
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# average data
self.scan = sdutil.doaverage(self.scan, self.scanaverage, self.timeaverage, self.tweight, self.polaverage, self.pweight)
# restore fluxunit
self.fluxunit = self.fluxunit_saved
del self.fluxunit_saved
self.result = dict.fromkeys(['nfit','peak','cent','fwhm'],[])
self.fitparams = []
if self.assert_no_channel_selection_in_spw(mode='result'):
maskline_dict = self.scan.parse_spw_selection(self.spw)
valid_spw_list = []
for (k,v) in maskline_dict.items():
if len(v) > 0 and numpy.all(numpy.array(map(len, v)) > 0):
valid_spw_list.append(k)
sel_org = self.scan.get_selection()
for irow in xrange(self.scan.nrow()):
scantab = self.scan.get_row(irow, insitu=False)
spw = scantab.getif(0)
if not spw in valid_spw_list:
continue
maskline = maskline_dict[spw]
self.maskline = maskline
self.__set_linelist(scantab)
self.__fit(scantab)
ifs_org = sel_org.get_ifs()
ifs_new = list(set(ifs_org) & set(valid_spw_list))
sel_org.set_ifs(ifs_new)
self.scan.set_selection(sel_org)
else:
self.maskline = []
self.__set_linelist()
self.__fit()
def execute(self):
# calibration stage
casalog.post("*** sdcal stage ***")
self.verify = self.verifycal
engine = task_sdcal.sdcal_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
## task_sdcal.prior_plot(self.scan, self.plotlevel)
## self.scan = task_sdcal.docalibration(self.scan, self.calmode,
## self.fraction,
## self.noff, self.width,
## self.elongated,
## self.markonly, self.plotpointings,
## self.verifycal)
# apply input parameters
self.set_to_scan()
# opacity correction
sdutil.doopacity(self.scan, self.tau)
## channel splitting
#sdutil.dochannelrange(self.scan, self.channelrange)
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# averaging stage
if self.average:
self.scan = sdutil.doaverage(self.scan, self.scanaverage,
self.timeaverage, self.tweight,
self.polaverage, self.pweight,
self.averageall)
else:
casalog.post("No averaging was applied...")
## task_sdcal.posterior_plot(self.scan, self.project, self.plotlevel)
engine.finalize()
del engine
# smoothing stage
casalog.post("")
casalog.post("*** sdsmooth stage ***")
if self.kernel.lower() not in ['none', '']:
self.verify = self.verifysm
engine = task_sdaverage.sdsmooth_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
engine.finalize()
del engine
else:
casalog.post("No smoothing was applied...")
# baseline stage
casalog.post("")
casalog.post("*** sdbaseline stage ***")
if self.blfunc != 'none':
self.verify = self.verifybl
self.verbose = self.verbosebl
engine = task_sdbaseline.sdbaseline_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
engine.finalize()
del engine
else:
casalog.post("No baseline subtraction was applied...")
def execute(self):
# calibration stage
casalog.post( "*** sdcal stage ***" )
self.verify = self.verifycal
engine = task_sdcal.sdcal_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
## task_sdcal.prior_plot(self.scan, self.plotlevel)
## self.scan = task_sdcal.docalibration(self.scan, self.calmode,
## self.fraction,
## self.noff, self.width,
## self.elongated,
## self.markonly, self.plotpointings,
## self.verifycal)
# apply input parameters
self.set_to_scan()
# opacity correction
sdutil.doopacity(self.scan, self.tau)
## channel splitting
#sdutil.dochannelrange(self.scan, self.channelrange)
#WORKAROUND for new tasks (in future this should be done in sdutil)
if not self.timeaverage: self.scanaverage = False
# averaging stage
if self.average:
self.scan = sdutil.doaverage(self.scan, self.scanaverage,
self.timeaverage, self.tweight,
self.polaverage, self.pweight,
self.averageall)
else:
casalog.post( "No averaging was applied..." )
## task_sdcal.posterior_plot(self.scan, self.project, self.plotlevel)
engine.finalize()
del engine
# smoothing stage
casalog.post( "" )
casalog.post( "*** sdsmooth stage ***" )
if self.kernel.lower() not in ['none', '']:
self.verify = self.verifysm
engine = task_sdaverage.sdsmooth_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
engine.finalize()
del engine
else:
casalog.post( "No smoothing was applied..." )
# baseline stage
casalog.post( "" )
casalog.post( "*** sdbaseline stage ***")
if self.blfunc != 'none':
self.verify = self.verifybl
self.verbose = self.verbosebl
engine = task_sdbaseline.sdbaseline_engine(self)
engine.initialize()
engine.execute()
## self.scan = engine.get_result()
engine.finalize()
del engine
else:
casalog.post( "No baseline subtraction was applied..." )
